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danielmartinec commited on Sep 4

Commit

ad352b2

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1 Parent(s): 7627841

Adding a bear classifier app

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Files changed (4) hide show

.gitignore +2 -0
app.ipynb +364 -0
export.pkl +3 -0
minima/app.py +26 -0

.gitignore ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ .env
2	+ .ipynb_checkpoints

app.ipynb ADDED Viewed

	@@ -0,0 +1,364 @@

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "61e04301-38df-4884-b6a5-22c9ea0b8f42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "eea0a34c-610b-4cda-a0d0-d33fc5a4d8c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "56b1b84c-124c-4a00-b3a9-825a73b3176c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
+      "image/png": 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",
+      "text/plain": [
+       "PILImage mode=RGB size=192x128"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "im = PILImage.create('grizzly.jpg')\n",
+    "im.thumbnail((192,192))\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "64fb2afb-d5ae-4fe9-8ede-f269fc731277",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "learn = load_learner('export.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "458d4f3f-44c7-40d1-b3f4-ec32505bcd3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('grizzly', tensor(1), tensor([9.2093e-04, 9.9894e-01, 1.3686e-04]))"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "learn.predict(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a8b789a2-2333-44ad-b786-ba8d85cc9225",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "categories = ('grizzly', 'black', 'teddy')\n",
+    "\n",
+    "def classify_image(im):\n",
+    "    pred, idx, probs = learn.predict(im)\n",
+    "    return dict(zip(categories, map(float, probs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "1faf012d-8159-4aae-b3a9-9c17751fc187",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'grizzly': 0.0009209336130879819,\n",
+       " 'black': 0.9989421963691711,\n",
+       " 'teddy': 0.00013686473539564759}"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classify_image(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "5e10c092-c82d-4d85-8cb9-c7dc791269fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "image = gr.Image(width=192, height=192)\n",
+    "label = gr.Label()\n",
+    "examples = ['grizzly.jpg', 'black.jpg', 'teddy.jpg', 'dunno.jpg']\n",
+    "\n",
+    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
+    "intf.launch(inline=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2017b1ba-f8d3-4a1d-98da-f52ce147df14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nbdev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "9e1976f1-79bb-496a-85e0-107766999c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nb_export('app.ipynb')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "ca8e3d8d-44ab-4d9b-be1b-62735cd85c63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nbdev.migrate import migrate_nb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "098fb721-a743-4cb7-aebd-2b50c338371a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0mmigrate_nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mSource:\u001b[0m   \n",
+       "\u001b[0;32mdef\u001b[0m \u001b[0mmigrate_nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0;34m\"Migrate Notebooks from nbdev v1 and fastpages.\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mnbp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNBProcessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mFrontmatterProc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMigrateProc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_repl_v1shortcuts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_repl_v1dir\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrm_directives\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mnbp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0;32mif\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mwrite_nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mnbp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/minima/.env/lib/python3.12/site-packages/nbdev/migrate.py\n",
+       "\u001b[0;31mType:\u001b[0m      function"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "??migrate_nb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "021b2a42-4ddd-472d-8597-f689e777d7ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#migrate_nb('app.ipynb', overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "452ffe22-1fa6-4b67-bc2e-eb03cd60598c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

export.pkl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:c27bbdca18dac9168974fd5a1509c5f39eb0e208b7333302fa2767000300b9a2
+size 46974334

minima/app.py ADDED Viewed

	@@ -0,0 +1,26 @@

+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+# %% ../app.ipynb 1
+from fastai.vision.all import *
+import gradio as gr
+# %% ../app.ipynb 3
+learn = load_learner('export.pkl')
+# %% ../app.ipynb 5
+categories = ('grizzly', 'black', 'teddy')
+def classify_image(im):
+    pred, idx, probs = learn.predict(im)
+    return dict(zip(categories, map(float, probs)))
+# %% ../app.ipynb 7
+image = gr.Image(width=192, height=192)
+label = gr.Label()
+examples = ['grizzly.jpg', 'black.jpg', 'teddy.jpg', 'dunno.jpg']
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)